OpenRaiser/Intern-Atlas huggingface dataset:
Density-Informed VAE (DiVAE): Reliable Log-Prior Probability via Density Alignment Regularization
Venue: arXiv cs.LG 2025
Year: 2025

We introduce Density-Informed VAE (DiVAE), a lightweight, data-driven regularizer that aligns the VAE log-prior probability $\log p_Z(z)$ with a log-density estimated from data. Standard VAEs match latents to a simple prior, overlooking density structure in the data-space. DiVAE encourages the encoder to allocate posterior mass in proportion to data-space density and, when the prior is learnable, nudges the prior toward high-density regions. This is realized by adding a robust, precision-weighted penalty to the ELBO, incurring negligible computational overhead. On synthetic datasets, DiVAE (i) improves distributional alignment of latent log-densities to its ground truth counterpart, (ii) improves prior coverage, and (iii) yields better OOD uncertainty calibration. On MNIST, DiVAE improves alignment of the prior with external estimates of the density, providing better interpretability, and improves OOD detection for learnable priors.
Autonomous Agents and Policy Compliance: A Framework for Reasoning About Penalties
Venue: arXiv cs.AI 2025
Year: 2025

This paper presents a logic programming-based framework for policy-aware autonomous agents that can reason about potential penalties for non-compliance and act accordingly. While prior work has primarily focused on ensuring compliance, our approach considers scenarios where deviating from policies may be necessary to achieve high-stakes goals. Additionally, modeling non-compliant behavior can assist policymakers by simulating realistic human decision-making. Our framework extends Gelfond and Lobo's Authorization and Obligation Policy Language (AOPL) to incorporate penalties and integrates Answer Set Programming (ASP) for reasoning. Compared to previous approaches, our method ensures well-formed policies, accounts for policy priorities, and enhances explainability by explicitly identifying rule violations and their consequences. Building on the work of Harders and Inclezan, we introduce penalty-based reasoning to distinguish between non-compliant plans, prioritizing those with minimal repercussions. To support this, we develop an automated translation from the extended AOPL into ASP and refine ASP-based planning algorithms to account for incurred penalties. Experiments in two domains demonstrate that our framework generates higher-quality plans that avoid harmful actions while, in some cases, also improving computational efficiency. These findings underscore its potential for enhancing autonomous decision-making and informing policy refinement. Under consideration in Theory and Practice of Logic Programming (TPLP).
MUT3R: Motion-aware Updating Transformer for Dynamic 3D Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Recent stateful recurrent neural networks have achieved remarkable progress on static 3D reconstruction but remain vulnerable to motion-induced artifacts, where non-rigid regions corrupt attention propagation between the spatial memory and image feature. By analyzing the internal behaviors of the state and image token updating mechanism, we find that aggregating self-attention maps across layers reveals a consistent pattern: dynamic regions are naturally down-weighted, exposing an implicit motion cue that the pretrained transformer already encodes but never explicitly uses. Motivated by this observation, we introduce MUT3R, a training-free framework that applies the attention-derived motion cue to suppress dynamic content in the early layers of the transformer during inference. Our attention-level gating module suppresses the influence of dynamic regions before their artifacts propagate through the feature hierarchy. Notably, we do not retrain or fine-tune the model; we let the pretrained transformer diagnose its own motion cues and correct itself. This early regulation stabilizes geometric reasoning in streaming scenarios and leads to improvements in temporal consistency and camera pose robustness across multiple dynamic benchmarks, offering a simple and training-free pathway toward motion-aware streaming reconstruction.
Technical Report on Text Dataset Distillation
Venue: arXiv cs.LG 2025
Year: 2025

In the vision domain, dataset distillation arises as a technique to condense a large dataset into a smaller synthetic one that exhibits a similar result in the training process. While image data presents an extensive literature of distillation methods, text dataset distillation has fewer works in comparison. Text dataset distillation initially grew as an adaptation of efforts from the vision universe, as the particularities of the modality became clear obstacles, it rose into a separate branch of research. Several milestones mark the development of this area, such as the introduction of methods that use transformer models, the generation of discrete synthetic text, and the scaling to decoder-only models with over 1B parameters. Despite major advances in modern approaches, the field remains in a maturing phase, with room for improvement on benchmarking standardization, approaches to overcome the discrete nature of text, handling complex tasks, and providing explicit examples of real-world applications. In this report, we review past and recent advances in dataset distillation for text, highlighting different distillation strategies, key contributions, and general challenges.
Diagonalizing the Softmax: Hadamard Initialization for Tractable Cross-Entropy Dynamics
Venue: arXiv cs.LG 2025
Year: 2025

Cross-entropy (CE) training loss dominates deep learning practice, yet existing theory often relies on simplifications, either replacing it with squared loss or restricting to convex models, that miss essential behavior. CE and squared loss generate fundamentally different dynamics, and convex linear models cannot capture the complexities of non-convex optimization. We provide an in-depth characterization of multi-class CE optimization dynamics beyond the convex regime by analyzing a canonical two-layer linear neural network with standard-basis vectors as inputs: the simplest non-convex extension for which the implicit bias remained unknown. This model coincides with the unconstrained features model used to study neural collapse, making our work the first to prove that gradient flow on CE converges to the neural collapse geometry. We construct an explicit Lyapunov function that establishes global convergence, despite the presence of spurious critical points in the non-convex landscape. A key insight underlying our analysis is an inconspicuous finding: Hadamard Initialization diagonalizes the softmax operator, freezing the singular vectors of the weight matrices and reducing the dynamics entirely to their singular values. This technique opens a pathway for analyzing CE training dynamics well beyond our specific setting considered here.
Non-Linear Determinants of Pedestrian Injury Severity: Evidence from Administrative Data in Great Britain
Venue: arXiv cs.CY 2025
Year: 2025

This study investigates the non-linear determinants of pedestrian injury severity using administrative data from Great Britain's 2023 STATS19 dataset. To address inherent data-quality challenges, including missing information and substantial class imbalance, we employ a rigorous preprocessing pipeline utilizing mode imputation and Synthetic Minority Over-sampling (SMOTE). We utilize non-parametric ensemble methods (Random Forest and XGBoost) to capture complex interactions and heterogeneity often missed by linear models, while Shapley Additive Explanations are employed to ensure interpretability and isolate marginal feature effects. Our analysis reveals that vehicle count, speed limits, lighting, and road surface conditions are the primary predictors of severity, with police attendance and junction characteristics further distinguishing severe collisions. Spatially, while pedestrian risk is concentrated in dense urban Local Authority Districts (LADs), we identify that certain rural LADs experience disproportionately severe outcomes conditional on a collision occurring. These findings underscore the value of combining spatial analysis with interpretable machine learning to guide geographically targeted speed management, infrastructure investment, and enforcement strategies.
Convergence for Discrete Parameter Update Schemes
Venue: arXiv cs.LG 2025
Year: 2025

Modern deep learning models require immense computational resources, motivating research into low-precision training. Quantised training addresses this by representing training components in low-bit integers, but typically relies on discretising real-valued updates. We introduce an alternative approach where the update rule itself is discrete, avoiding the quantisation of continuous updates by design. We establish convergence guarantees for a general class of such discrete schemes, and present a multinomial update rule as a concrete example, supported by empirical evaluation. This perspective opens new avenues for efficient training, particularly for models with inherently discrete structure.
Fare Comparison App of Uber, Ola and Rapido
Venue: arXiv cs.LG 2025
Year: 2025

In todays increasing world, it is very important to have good hailing services like Ola, Uber, and Rapido as it is very essential for our daily transportation. Users often face difficulties in choosing the most appropriate and efficient ride that would lead to both cost-effective and would take us to our destination in less time. This project provides you with the web application that helps you to select the most beneficial ride for you by providing users with the fare comparison between Ola, Uber, Rapido for the destination entered by the user. The backend is use to fetch the data, providing users with the fare comparison for the ride and finally providing with the best option using Python. This research paper also addresses the problem and challenges faced in accessing the data using APIs, Android Studios emulator, Appium and location comparison. Thus, the aim of the project is to provide transparency to the users in ride-hailing services and increase efficiency and provide users with better experience.
Formal Specification for Fast ACS: Low-Latency File-Based Ordered Message Delivery at Scale
Venue: arXiv cs.DC 2025
Year: 2025

Low-latency message delivery is crucial for real-time systems. Data originating from a producer must be delivered to consumers, potentially distributed in clusters across metropolitan and continental boundaries. With the growing scale of computing, there can be several thousand consumers of the data. Such systems require a robust messaging system capable of transmitting messages containing data across clusters and efficiently delivering them to consumers. The system must offer guarantees like ordering and at-least-once delivery while avoiding overload on consumers, allowing them to consume messages at their own pace. This paper presents the design of Fast ACS (an abbreviation for Ads Copy Service), a file-based ordered message delivery system that leverages a combination of two-sided (inter-cluster) and one-sided (intra-cluster) communication primitives - namely, Remote Procedure Call and Remote Memory Access, respectively - to deliver messages. The system has been successfully deployed to dozens of production clusters and scales to accommodate several thousand consumers within each cluster, which amounts to Tbps-scale intra-cluster consumer traffic at peak. Notably, Fast ACS delivers messages to consumers across the globe within a few seconds or even sub-seconds (p99) based on the message volume and consumer scale, at a low resource cost.
Retrieval-Augmented Few-Shot Prompting Versus Fine-Tuning for Code Vulnerability Detection
Venue: arXiv cs.SE 2025
Year: 2025

Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in specialized tasks. However, its effectiveness depends heavily on the selection and quality of in-context examples, particularly in complex domains. In this work, we examine retrieval-augmented prompting as a strategy to improve few-shot performance in code vulnerability detection, where the goal is to identify one or more security-relevant weaknesses present in a given code snippet from a predefined set of vulnerability categories. We perform a systematic evaluation using the Gemini-1.5-Flash model across three approaches: (1) standard few-shot prompting with randomly selected examples, (2) retrieval-augmented prompting using semantically similar examples, and (3) retrieval-based labeling, which assigns labels based on retrieved examples without model inference. Our results show that retrieval-augmented prompting consistently outperforms the other prompting strategies. At 20 shots, it achieves an F1 score of 74.05% and a partial match accuracy of 83.90%. We further compare this approach against zero-shot prompting and several fine-tuned models, including Gemini-1.5-Flash and smaller open-source models such as DistilBERT, DistilGPT2, and CodeBERT. Retrieval-augmented prompting outperforms both zero-shot (F1 score: 36.35%, partial match accuracy: 20.30%) and fine-tuned Gemini (F1 score: 59.31%, partial match accuracy: 53.10%), while avoiding the training time and cost associated with model fine-tuning. On the other hand, fine-tuning CodeBERT yields higher performance (F1 score: 91.22%, partial match accuracy: 91.30%) but requires additional training, maintenance effort, and resources.
AI-Enabled grading with near-domain data for scaling feedback with human-level accuracy
Venue: arXiv cs.CY 2025
Year: 2025

Constructed-response questions are crucial to encourage generative processing and test a learner's understanding of core concepts. However, the limited availability of instructor time, large class sizes, and other resource constraints pose significant challenges in providing timely and detailed evaluation, which is crucial for a holistic educational experience. In addition, providing timely and frequent assessments is challenging since manual grading is labor intensive, and automated grading is complex to generalize to every possible response scenario. This paper proposes a novel and practical approach to grade short-answer constructed-response questions. We discuss why this problem is challenging, define the nature of questions on which our method works, and finally propose a framework that instructors can use to evaluate their students' open-responses, utilizing near-domain data like data from similar questions administered in previous years. The proposed method outperforms the state of the art machine learning models as well as non-fine-tuned large language models like GPT 3.5, GPT 4, and GPT 4o by a considerable margin of over 10-20% in some cases, even after providing the LLMs with reference/model answers. Our framework does not require pre-written grading rubrics and is designed explicitly with practical classroom settings in mind. Our results also reveal exciting insights about learning from near-domain data, including what we term as accuracy and data advantages using human-labeled data, and we believe this is the first work to formalize the problem of automated short answer grading based on the near-domain data.
Artificial Intelligence Competence of K-12 Students Shapes Their AI Risk Perception: A Co-occurrence Network Analysis
Venue: arXiv cs.CY 2025
Year: 2025

As artificial intelligence (AI) becomes increasingly integrated into education, understanding how students perceive its risks is essential for supporting responsible and effective adoption. This research aimed to examine the relationships between perceived AI competence and risks among Finnish K-12 upper secondary students (n = 163) by utilizing a co-occurrence analysis. Students reported their self-perceived AI competence and concerns related to AI across systemic, institutional, and personal domains. The findings showed that students with lower competence emphasized personal and learning-related risks, such as reduced creativity, lack of critical thinking, and misuse, whereas higher-competence students focused more on systemic and institutional risks, including bias, inaccuracy, and cheating. These differences suggest that students' self-reported AI competence is related to how they evaluate both the risks and opportunities associated with artificial intelligence in education (AIED). The results of this study highlight the need for educational institutions to incorporate AI literacy into their curricula, provide teacher guidance, and inform policy development to ensure personalized opportunities for utilization and equitable integration of AI into K-12 education.
Humanity in the Age of AI: Reassessing 2025's Existential-Risk Narratives
Venue: arXiv cs.CY 2025
Year: 2025

Two 2025 publications, "AI 2027" (Kokotajlo et al., 2025) and "If Anyone Builds It, Everyone Dies" (Yudkowsky & Soares, 2025), assert that superintelligent artificial intelligence will almost certainly destroy or render humanity obsolete within the next decade. Both rest on the classic chain formulated by Good (1965) and Bostrom (2014): intelligence explosion, superintelligence, lethal misalignment. This article subjects each link to the empirical record of 2023-2025. Sixty years after Good's speculation, none of the required phenomena (sustained recursive self-improvement, autonomous strategic awareness, or intractable lethal misalignment) have been observed. Current generative models remain narrow, statistically trained artefacts: powerful, opaque, and imperfect, but devoid of the properties that would make the catastrophic scenarios plausible. Following Whittaker (2025a, 2025b, 2025c) and Zuboff (2019, 2025), we argue that the existential-risk thesis functions primarily as an ideological distraction from the ongoing consolidation of surveillance capitalism and extreme concentration of computational power. The thesis is further inflated by the 2025 AI speculative bubble, where trillions in investments in rapidly depreciating "digital lettuce" hardware (McWilliams, 2025) mask lagging revenues and jobless growth rather than heralding superintelligence. The thesis remains, in November 2025, a speculative hypothesis amplified by a speculative financial bubble rather than a demonstrated probability.
Can machines perform a qualitative data analysis? Reading the debate with Alan Turing
Venue: arXiv cs.CY 2025
Year: 2025

This paper reflects on the literature that rejects the use of Large Language Models (LLMs) in qualitative data analysis. It illustrates through empirical evidence as well as critical reflections why the current critical debate is focusing on the wrong problems. The paper proposes that the focus of researching the use of the LLMs for qualitative analysis is not the method per se, but rather the empirical investigation of an artificial system performing an analysis. The paper builds on the seminal work of Alan Turing and reads the current debate using key ideas from Turing "Computing Machinery and Intelligence". This paper therefore reframes the debate on qualitative analysis with LLMs and states that rather than asking whether machines can perform qualitative analysis in principle, we should ask whether with LLMs we can produce analyses that are sufficiently comparable to human analysts. In the final part the contrary views to performing qualitative analysis with LLMs are analysed using the same writing and rhetorical style that Turing used in his seminal work, to discuss the contrary views to the main question.
ASCIIBench: Evaluating Language-Model-Based Understanding of Visually-Oriented Text
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) have demonstrated several emergent behaviors with scale, including reasoning and fluency in long-form text generation. However, they continue to struggle with tasks requiring precise spatial and positional reasoning. ASCII art, a symbolic medium where characters encode structure and form, provides a unique probe of this limitation. We introduce ASCIIBench, a novel benchmark for evaluating both the generation and classification of ASCII-text images. ASCIIBench consists of a filtered dataset of 5,315 class-labeled ASCII images and is, to our knowledge, the first publicly available benchmark of its kind. Alongside the dataset, we release weights for a fine-tuned CLIP model adapted to capture ASCII structure, enabling the evaluation of LLM-generated ASCII art. Our analysis shows that cosine similarity over CLIP embeddings fails to separate most ASCII categories, yielding chance-level performance even for low-variance classes. In contrast, classes with high internal mean similarity exhibit clear discriminability, revealing that the bottleneck lies in representation rather than generational variance. These findings position ASCII art as a stress test for multimodal representations and motivate the development of new embedding methods or evaluation metrics tailored to symbolic visual modalities. All resources are available at https://github.com/ASCIIBench/ASCIIBench.
Tipping the Dominos: Topology-Aware Multi-Hop Attacks on LLM-Based Multi-Agent Systems
Venue: arXiv cs.CR 2025
Year: 2025

LLM-based multi-agent systems (MASs) have reshaped the digital landscape with their emergent coordination and problem-solving capabilities. However, current security evaluations of MASs are still confined to limited attack scenarios, leaving their security issues unclear and likely underestimated. To fill this gap, we propose TOMA, a topology-aware multi-hop attack scheme targeting MASs. By optimizing the propagation of contamination within the MAS topology and controlling the multi-hop diffusion of adversarial payloads originating from the environment, TOMA unveils new and effective attack vectors without requiring privileged access or direct agent manipulation. Experiments demonstrate attack success rates ranging from 40% to 78% across three state-of-the-art MAS architectures: \textsc{Magentic-One}, \textsc{LangManus}, and \textsc{OWL}, and five representative topologies, revealing intrinsic MAS vulnerabilities that may be overlooked by existing research. Inspired by these findings, we propose a conceptual defense framework based on topology trust, and prototype experiments show its effectiveness in blocking 94.8% of adaptive and composite attacks.
Generalized Event Partonomy Inference with Structured Hierarchical Predictive Learning
Venue: arXiv cs.CV 2025
Year: 2025

Humans naturally perceive continuous experience as a hierarchy of temporally nested events, fine-grained actions embedded within coarser routines. Replicating this structure in computer vision requires models that can segment video not just retrospectively, but predictively and hierarchically. We introduce PARSE, a unified framework that learns multiscale event structure directly from streaming video without supervision. PARSE organizes perception into a hierarchy of recurrent predictors, each operating at its own temporal granularity: lower layers model short-term dynamics while higher layers integrate longer-term context through attention-based feedback. Event boundaries emerge naturally as transient peaks in prediction error, yielding temporally coherent, nested partonomies that mirror the containment relations observed in human event perception. Evaluated across three benchmarks, Breakfast Actions, 50 Salads, and Assembly 101, PARSE achieves state-of-the-art performance among streaming methods and rivals offline baselines in both temporal alignment (H-GEBD) and structural consistency (TED, hF1). The results demonstrate that predictive learning under uncertainty provides a scalable path toward human-like temporal abstraction and compositional event understanding.